SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

2023 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS, IJCB(2023)

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摘要
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.
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关键词
Training Data,Attack Detection,Synthetic Training Data,Presentation Attack,Presentation Attack Detection,Face Presentation Attack,Face Presentation Attack Detection,Privacy,Biometric,Receiver Operating Characteristic Curve,Local Features,Data Augmentation,Cross-entropy Loss,Face Recognition,Baseline Methods,Gaussian Mixture Model,Types Of Attacks,Cross-entropy Loss Function,Average Rank,Binary Cross-entropy Loss,Message Authentication,Replay Attacks,Color Jittering,Raw Features,Final Ranking,JPEG Compression,Benchmark Evaluation,Equal Error Rate,Data Augmentation Techniques,Adam Optimizer
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